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A dynamic model of the influence of rotation and crop management on the disease development of eyespot. Proposal of cropping systems with low disease risk
Institution:1. Centre of Plant Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia 4072, Australia;2. Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi 835215, India;3. National Phytotron Facility, New Delhi 110012, India;4. Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut 250004, India;1. Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan;2. Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan;3. Institute of Crop Science, Chinese Academy of Agricultural Sciences (CAAS) & CIMMYT-China office, 12 Zhongguancun South Street, Beijing 100081, China
Abstract:A dynamic model of the effect of rotation and crop management on the frequency of plants infected by eyespot (anamorph Pseudocercosporella herpotrichoides, teleomorph Tapesia yallundae) in a field is proposed and its parameters are estimated on a series of experiments in France during two years. A first equation estimates disease frequency as a function of thermal time and of two parameters associated to the primary (from infectious crop residues) and secondary (from living diseased plants) infection cycles. Two other equations are proposed to estimate the values of the two infection cycle parameters as a function of macro-environment and cropping system; interactions between cultivation techniques were integrated using multiplicative equations. The primary infection cycle parameter depended on crop rotation, soil tillage, sowing date, tiller number per plant and available nitrogen. The secondary infection cycle parameter depended on tiller number per plant. The macro-environment effect on the two parameters is consistent with epidemiological models. The proposed model allows to choose those cropping systems that minimise disease risk for a given set of environmental and technical constraints.
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